Map/reduce is a programming model for processing large data sets. Typically, map/reduce is used on clusters of computers, such as clusters of storage servers in a distributed file system. To reduce network traffic, a map/reduce-based application can determine the physical location of a file and have the storage servers, which are closest to the file, process the file for a job. For example, a file system may have clusters of storage servers, that each include a master node and one or more worker nodes. During the “map” phase, a master node can receive a job request, from the map/reduce-based application, to perform an operation using a file. The master node can divide the job into smaller sub-jobs, and can distribute the sub-jobs to the worker nodes that are closest to the file. The worker nodes can process the sub-jobs in parallel and can pass the result back to the master node. During the “reduce” phase, the master node can collect the results for the sub-jobs and combine the results to form the output for the job request. The job request may have included a location identifier of the physical location of the file that should be processed for the requested job. Traditionally, a map/reduce-based application can query a centralized metadata server which would provide the physical location of the input file to the map/reduce-based application. At times, a centralized metadata server may be a bottleneck and may be single point of failure.